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Complete resource pooling of a load-balancing policy for a network of battery swapping stations

Author

Listed:
  • Fiona Sloothaak

    (Eindhoven University of Technology)

  • James Cruise

    (Riverlane)

  • Seva Shneer

    (Heriot-Watt University
    Novosibirsk State University)

  • Maria Vlasiou

    (Eindhoven University of Technology
    University of Twente)

  • Bert Zwart

    (Eindhoven University of Technology
    Centrum Wiskunde & Informatica)

Abstract

To reduce carbon emission in the transportation sector, there is currently a steady move taking place to an electrified transportation system. This brings about various issues for which a promising solution involves the construction and operation of a battery swapping infrastructure rather than in-vehicle charging of batteries. In this paper, we study a closed Markovian queueing network that allows for spare batteries under a dynamic arrival policy. We propose a provisioning rule for the capacity levels and show that these lead to near-optimal resource utilization, while guaranteeing good quality-of-service levels for electric vehicle users. Key in the derivations is to prove a state-space collapse result, which in turn implies that performance levels are as good as if there would have been a single station with an aggregated number of resources, thus achieving complete resource pooling.

Suggested Citation

  • Fiona Sloothaak & James Cruise & Seva Shneer & Maria Vlasiou & Bert Zwart, 2021. "Complete resource pooling of a load-balancing policy for a network of battery swapping stations," Queueing Systems: Theory and Applications, Springer, vol. 99(1), pages 65-120, October.
  • Handle: RePEc:spr:queues:v:99:y:2021:i:1:d:10.1007_s11134-021-09707-w
    DOI: 10.1007/s11134-021-09707-w
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    References listed on IDEAS

    as
    1. Galit B. Yom-Tov & Avishai Mandelbaum, 2014. "Erlang-R: A Time-Varying Queue with Reentrant Customers, in Support of Healthcare Staffing," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 283-299, May.
    2. J. G. Dai & Tolga Tezcan, 2011. "State Space Collapse in Many-Server Diffusion Limits of Parallel Server Systems," Mathematics of Operations Research, INFORMS, vol. 36(2), pages 271-320, May.
    3. Sun, Bo & Sun, Xu & Tsang, Danny H.K. & Whitt, Ward, 2019. "Optimal battery purchasing and charging strategy at electric vehicle battery swap stations," European Journal of Operational Research, Elsevier, vol. 279(2), pages 524-539.
    4. Sem Borst & Avi Mandelbaum & Martin I. Reiman, 2004. "Dimensioning Large Call Centers," Operations Research, INFORMS, vol. 52(1), pages 17-34, February.
    5. Francis de Véricourt & Otis B. Jennings, 2008. "Dimensioning Large-Scale Membership Services," Operations Research, INFORMS, vol. 56(1), pages 173-187, February.
    6. Bo Zhang & Johan S. H. van Leeuwaarden & Bert Zwart, 2012. "Staffing Call Centers with Impatient Customers: Refinements to Many-Server Asymptotics," Operations Research, INFORMS, vol. 60(2), pages 461-474, April.
    7. Shlomo Halfin & Ward Whitt, 1981. "Heavy-Traffic Limits for Queues with Many Exponential Servers," Operations Research, INFORMS, vol. 29(3), pages 567-588, June.
    8. Francis de Véricourt & Otis B. Jennings, 2011. "Nurse Staffing in Medical Units: A Queueing Perspective," Operations Research, INFORMS, vol. 59(6), pages 1320-1331, December.
    9. Tolga Tezcan, 2008. "Optimal Control of Distributed Parallel Server Systems Under the Halfin and Whitt Regime," Mathematics of Operations Research, INFORMS, vol. 33(1), pages 51-90, February.
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